The present invention relates generally to the field of image processing and image segmentation. More particularly, the invention relates to a technique for processing image data that permits more flexible processing image data to render the processed images more clear, intuitive and useful.
Many applications and settings exist for digital image processing. By way of example, many images are created in the medical diagnostics contexts by use of specific imaging modalities or combination of modalities. These include modalities such as magnetic resonance imaging (MRI) systems, computed tomography (CT) imaging systems, positron emission tomography (PET) imaging systems, ultrasound imaging systems, tomosynthesis systems, X-ray systems, and so forth. These and other modalities and the devices are used in other settings, such as part and partial inspection, quality control, and more generally in the production of processing of any digital imaging.
Digital image data is generally collected and processed in many ways to form data appropriate for reconstruction of a useful image. The image is comprised of an array of discrete picture elements or pixels that, when viewed, form a composite image. The pixelated data may be processed in different ways depending upon the nature of the data, the type of acquisition system used to create the data, the subject matter visible in the reconstructed image, and more generally the intended use of the reconstructed images. A wide range of such processing techniques is known and presently in use throughout the imaging community. Such conventional processing techniques are not, however, without drawbacks.
For example, many imaging techniques make use of various forms of mask or maps formulated by analysis of image data and decisions on classification of the image data. One type of map commonly utilized divides image data into structures and non-structures for example. When such divisions are made, it is common in image processing to treat one set of data, corresponding to a particular mask, in one manner and another set of data, typically exclusive of the data in the first mask, in a different manner. This is particularly true of image processing that divides images into structures and non-structures. However, such masking and processing of data takes many forms and may be performed several times on the same data to enhance the image reconstructed from the processed data.
The practice of subdividing and analyzing image data by type, such as by classification of individual pixels as representing structure or non-structure is highly effective in processing certain types of images, and renders excellent results that can be used for careful analysis and, in the medical context, diagnosis and treatment. However, for more nuanced images, or images where such classification is somewhat unclear, the subdivisions may result in inadvertently emphasizing or de-emphasizing portions of the image data that actually results in a reconstructed image that is somewhat more difficult to analyze, such as due to loss of contrast, loss of detail, and so forth.
There is, at present, a need to combine multiple image processing operations, such as those performed on structures and non-structures in a seamless fashion such as to combine operations and reduce any tendency to produce artifacts or abrupt shifts from one operational mode to the other, impairing visualization. One method for solving this problem is to spatially transition from one operation to the other by blending one operation with the other based on spatial distance. While this approach addresses the transition-related artifacts, it does not correct for errors in the segmentation of the image data, as between structures and non-structures. Again, such errors cause many undesirable results in image visualization. In the case of structures, results can appear to enhance false structures, or, conversely, result in the loss of contrast where structures should be recognized.